Normalizing ingested signals

ABSTRACT

The present invention extends to methods, systems, and computer program products for normalizing ingested signals. In general, different types of raw signals including source data in different pluralities of data dimensions and including other characteristics are ingested. Per raw signal, a transdimensionality transform is applied to recode and normalize the source data into a normalized signal that includes normalized data in a common reduced plurality of dimensions including a time dimension, a location dimension, and a context dimension. Normalization can include inferring signal annotations from the source data and using the annotations and/or the other characteristics to derive time, location, and context dimensions. Derivation can include computing a probability of a real-world event and including the probability in the context dimension. An real-world event is detection from the normalized data in the time, location, and context dimensions and an entity is notified of the real-world event.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/038,537, entitled “Normalizing Ingested Signals”, filed Jul. 18, 2018which is incorporated herein in its entirety.

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/664,001, entitled “Normalizing Different TypesOf Ingested Signals Into A Common Format”, filed Apr. 27, 2018 which isincorporated herein in its entirety. This application claims the benefitof U.S. Provisional Patent Application Ser. No. 62/667,616, entitled“Normalizing Different Types Of Ingested Signals Into A Common Format”,filed May 7, 2018 which is incorporated herein in its entirety. Thisapplication claims the benefit of U.S. Provisional Patent ApplicationSer. No. 62/686,791 entitled, “Normalizing Signals”, filed Jun. 19, 2018which is incorporated herein in its entirety.

BACKGROUND 1. Background and Relevant Art

Data provided to computer systems can come from any number of differentsources, such as, for example, user input, files, databases,applications, sensors, social media systems, cameras, emergencycommunications, etc. In some environments, computer systems receive(potentially large volumes of) data from a variety of different domainsand/or verticals in a variety of different formats. When data isreceived from different sources and/or in different formats, it can bedifficult to efficiently and effectively derive intelligence from thedata.

Extract, transform, and load (ETL) refers to a technique that extractsdata from data sources, transforms the data to fit operational needs,and loads the data into an end target. ETL systems can be used tointegrate data from multiple varied sources, such as, for example, fromdifferent vendors, hosted on different computer systems, etc.

ETL is essentially an extract and then store process. Prior toimplementing an ETL solution, a user defines what (e.g., subset of) datais to be extracted from a data source and a schema of how the extracteddata is to be stored. During the ETL process, the defined (e.g., subsetof) data is extracted, transformed to the form of the schema (i.e.,schema is used on write), and loaded into a data store. To accessdifferent data from the data source, the user has to redefine what datais to be extracted. To change how data is stored, the user has to definea new schema.

ETL is beneficially because it allows a user to access a desired portionof data in a desired format. However, ETL can be cumbersome as dataneeds evolve. Each change to the extracted data and/or the data storageresults in the ETL process having to be restarted.

BRIEF SUMMARY

Examples extend to methods, systems, and computer program products fornormalizing ingested signals.

A raw signal, including source data in a plurality of dimensions, isingested. A transdimensionality transform is applied to the raw signalto recode and normalize the source data into a normalized signal thatincludes normalized data in a common reduced plurality of dimensionsincluding a time dimension, a location dimension, and a contextdimension. Recoding and normalizing include inferring a signalannotation from the source data and other signal characteristics of theraw signal.

Recoding and normalizing include deriving the time dimension, thelocation dimension, and the context dimension from the other signalcharacteristics and/or the signal annotation. Deriving the timedimension, the location dimension, and the context dimension includecomputing a probability value from the other signal characteristics andthat at least approximates a probability of a real-world event type.Deriving the time dimension, the location dimension, and the contextdimension includes inserting the probability into the context dimension;

A real-world event of the real-world event type is detected from thenormalized data in the time dimension, the location dimension, and thecontext dimension included in the normalized signal. An entity isnotified about the real-world event.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be obvious from the description, or maybe learned by practice. The features and advantages may be realized andobtained by means of the instruments and combinations particularlypointed out in the appended claims. These and other features andadvantages will become more fully apparent from the followingdescription and appended claims, or may be learned by practice as setforth hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionwill be rendered by reference to specific implementations thereof whichare illustrated in the appended drawings. Understanding that thesedrawings depict only some implementations and are not therefore to beconsidered to be limiting of its scope, implementations will bedescribed and explained with additional specificity and detail throughthe use of the accompanying drawings in which:

FIG. 1A illustrates an example computer architecture that facilitatesnormalizing ingesting signals.

FIG. 1B illustrates an example computer architecture that facilitatesdetecting events from normalized signals.

FIG. 2 illustrates a flow chart of an example method for normalizingingested signals.

FIGS. 3A, 3B, and 3C illustrate other example components that can beincluded in signal ingestion modules.

FIG. 4 illustrates a flow chart of an example method for normalizing aningested signal including time information, location information, andcontext information.

FIG. 5 illustrates a flow chart of an example method for normalizing aningested signal including time information and location information.

FIG. 6 illustrates a flow chart of an example method for normalizing aningested signal including time information.

DETAILED DESCRIPTION

Examples extend to methods, systems, and computer program products fornormalizing ingested signals.

Entities (e.g., parents, other family members, guardians, friends,teachers, social workers, first responders, hospitals, deliveryservices, media outlets, government entities, etc.) may desire to bemade aware of relevant events as close as possible to the events'occurrence (i.e., as close as possible to “moment zero”). Differenttypes of ingested signals (e.g., social media signals, web signals, andstreaming signals) can be used to detect events.

In general, signal ingestion modules ingest different types of rawstructured and/or raw unstructured signals on an ongoing basis.Different types of signals can include different data media types anddifferent data formats. Data media types can include audio, video,image, and text. Different formats can include text in XML, text inJavaScript Object Notation (JSON), text in RSS feed, plain text, videostream in Dynamic Adaptive Streaming over HTTP (DASH), video stream inHTTP Live Streaming (HLS), video stream in Real-Time Messaging Protocol(RTMP), other Multipurpose Internet Mail Extensions (MIME) types, etc.Handling different types and formats of data introduces inefficienciesinto subsequent event detection processes, including when determining ifdifferent signals relate to the same event.

Accordingly, the signal ingestion modules can normalize raw signalsacross multiple data dimensions to form normalized signals. Eachdimension can be a scalar value or a vector of values. In one aspect,raw signals are normalized into normalized signals having a Time,Location, Context (or “TLC”) dimensions.

A Time (T) dimension can include a time of origin or alternatively a“event time” of a signal. A Location (L) dimension can include alocation anywhere across a geographic area, such as, a country (e.g.,the United States), a State, a defined area, an impacted area, an areadefined by a geo cell, an address, etc.

A Context (C) dimension indicates circumstances surroundingformation/origination of a raw signal in terms that facilitateunderstanding and assessment of the raw signal. The Context (C)dimension of a raw signal can be derived from express as well asinferred signal features of the raw signal.

Signal ingestion modules can include one or more single sourceclassifiers. A single source classifier can compute a single sourceprobability for a raw signal from features of the raw signal. A singlesource probability can reflect a mathematical probability orapproximation of a mathematical probability (e.g., a percentage between0%-100%) of an event actually occurring. A single source classifier canbe configured to compute a single source probability for a single eventtype or to compute a single source probability for each of a pluralityof different event types. A single source classifier can compute asingle source probability using artificial intelligence, machinelearning, neural networks, logic, heuristics, etc.

As such, single source probabilities and corresponding probabilitydetails can represent a Context (C) dimension. Probability details canindicate (e.g., can include a hash field indicating) a probabilisticmodel and (express and/or inferred) signal features considered in asignal source probability calculation.

Thus, per signal type, signal ingestion modules determine Time (T), aLocation (L), and a Context (C) dimensions associated with a signal.Different ingestion modules can be utilized/tailored to determine T, L,and C dimensions associated with different signal types. Normalized (or“TLC”) signals can be forwarded to an event detection infrastructure.When signals are normalized across common dimensions subsequent eventdetection is more efficient and more effective.

Normalization of ingestion signals can include dimensionality reduction.Generally, “transdimensionality” transformations can be structured anddefined in a “TLC” dimensional model. Signal ingestion modules can applythe “transdimensionality” transformations to generic source data in rawsignals to re-encode the source data into normalized data having lowerdimensionality. Thus, each normalized signal can include a T vector, anL vector, and a C vector. At lower dimensionality, the complexity ofmeasuring “distances” between dimensional vectors across differentnormalized signals is reduced.

Concurrently with signal ingestion, an event detection infrastructureconsiders features of different combinations of normalized signals toattempt to identify events of interest to various parties. For example,the event detection infrastructure can determine that features ofmultiple different normalized signals collectively indicate an event ofinterest to one or more parties. Alternately, the event detectioninfrastructure can determine that features of one or more normalizedsignals indicate a possible event of interest to one or more parties.The event detection infrastructure then determines that features of oneor more other normalized signals validate the possible event as anactual event of interest to the one or more parties. Signal features caninclude: signal type, signal source, signal content, Time (T) dimension,Location (L) dimension, Context (C) dimension, other circumstances ofsignal creation, etc.

Implementations can comprise or utilize a special purpose orgeneral-purpose computer including computer hardware, such as, forexample, one or more computer and/or hardware processors (including anyof Central Processing Units (CPUs), and/or Graphical Processing Units(GPUs), general-purpose GPUs (GPGPUs), Field Programmable Gate Arrays(FPGAs), application specific integrated circuits (ASICs), TensorProcessing Units (TPUs)) and system memory, as discussed in greaterdetail below. Implementations also include physical and othercomputer-readable media for carrying or storing computer-executableinstructions and/or data structures. Such computer-readable media can beany available media that can be accessed by a general purpose or specialpurpose computer system. Computer-readable media that storecomputer-executable instructions are computer storage media (devices).Computer-readable media that carry computer-executable instructions aretransmission media. Thus, by way of example, and not limitation,implementations can comprise at least two distinctly different kinds ofcomputer-readable media: computer storage media (devices) andtransmission media.

Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM,Solid State Drives (“SSDs”) (e.g., RAM-based or Flash-based), ShingledMagnetic Recording (“SMR”) devices, Flash memory, phase-change memory(“PCM”), other types of memory, other optical disk storage, magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to store desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer.

In one aspect, one or more processors are configured to executeinstructions (e.g., computer-readable instructions, computer-executableinstructions, etc.) to perform any of a plurality of describedoperations. The one or more processors can access information fromsystem memory and/or store information in system memory. The one or moreprocessors can (e.g., automatically) transform information betweendifferent formats, such as, for example, between any of: raw signals,normalized signals, signal features, single source probabilities, times,time dimensions, locations, location dimensions, geo cells, geo cellentries, designated market areas (DMAs), contexts, location annotations,context annotations, classification tags, context dimensions, events,etc.

System memory can be coupled to the one or more processors and can storeinstructions (e.g., computer-readable instructions, computer-executableinstructions, etc.) executed by the one or more processors. The systemmemory can also be configured to store any of a plurality of other typesof data generated and/or transformed by the described components, suchas, for example, raw signals, normalized signals, signal features,single source probabilities, times, time dimensions, locations, locationdimensions, geo cells, geo cell entries, designated market areas (DMAs),contexts, location annotations, context annotations, classificationtags, context dimensions, events, etc.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope ofcomputer-readable media.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission media to computerstorage media (devices) (or vice versa). For example,computer-executable instructions or data structures received over anetwork or data link can be buffered in RAM within a network interfacemodule (e.g., a “NIC”), and then eventually transferred to computersystem RAM and/or to less volatile computer storage media (devices) at acomputer system. Thus, it should be understood that computer storagemedia (devices) can be included in computer system components that also(or even primarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions anddata which, in response to execution at a processor, cause a generalpurpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the described aspects maybe practiced in network computing environments with many types ofcomputer system configurations, including, personal computers, desktopcomputers, laptop computers, message processors, hand-held devices,wearable devices, multicore processor systems, multi-processor systems,microprocessor-based or programmable consumer electronics, network PCs,minicomputers, mainframe computers, mobile telephones, PDAs, tablets,routers, switches, and the like. The described aspects may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Further, where appropriate, functions described herein can be performedin one or more of: hardware, software, firmware, digital components, oranalog components. For example, one or more Field Programmable GateArrays (FPGAs) and/or one or more application specific integratedcircuits (ASICs) and/or one or more Tensor Processing Units (TPUs) canbe programmed to carry out one or more of the systems and proceduresdescribed herein. Hardware, software, firmware, digital components, oranalog components can be specifically tailor-designed for a higher speeddetection or artificial intelligence that can enable signal processing.In another example, computer code is configured for execution in one ormore processors, and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein purposes of illustration, and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices.

The described aspects can also be implemented in cloud computingenvironments. In this description and the following claims, “cloudcomputing” is defined as a model for enabling on-demand network accessto a shared pool of configurable computing resources. For example, cloudcomputing can be employed in the marketplace to offer ubiquitous andconvenient on-demand access to the shared pool of configurable computingresources (e.g., compute resources, networking resources, and storageresources). The shared pool of configurable computing resources can beprovisioned via virtualization and released with low effort or serviceprovider interaction, and then scaled accordingly.

A cloud computing model can be composed of various characteristics suchas, for example, on-demand self-service, broad network access, resourcepooling, rapid elasticity, measured service, and so forth. A cloudcomputing model can also expose various service models, such as, forexample, Software as a Service (“SaaS”), Platform as a Service (“PaaS”),and Infrastructure as a Service (“IaaS”). A cloud computing model canalso be deployed using different deployment models such as privatecloud, community cloud, public cloud, hybrid cloud, and so forth. Inthis description and in the following claims, a “cloud computingenvironment” is an environment in which cloud computing is employed.

In this description and the following claims, a “geo cell” is defined asa piece of “cell” in a spatical grid in any form. In one aspect, geocells are arranged in a hierarchical structure. Cells of differentgeometries can be used.

A “geohash” is an example of a “geo cell”.

In this description and the following claims, “geohash” is defined as ageocoding system which encodes a geographic location into a short stringof letters and digits. Geohash is a hierarchical spatial data structurewhich subdivides space into buckets of grid shape (e.g., a square).Geohashes offer properties like arbitrary precision and the possibilityof gradually removing characters from the end of the code to reduce itssize (and gradually lose precision). As a consequence of the gradualprecision degradation, nearby places will often (but not always) presentsimilar prefixes. The longer a shared prefix is, the closer the twoplaces are. geo cells can be used as a unique identifier and toapproximate point data (e.g., in databases).

In one aspect, a “geohash” is used to refer to a string encoding of anarea or point on the Earth. The area or point on the Earth may berepresented (among other possible coordinate systems) as alatitude/longitude or Easting/Northing—the choice of which is dependenton the coordinate system chosen to represent an area or point on theEarth. geo cell can refer to an encoding of this area or point, wherethe geo cell may be a binary string comprised of 0s and 1s correspondingto the area or point, or a string comprised of 0s, 1s, and a ternarycharacter (such as X)—which is used to refer to a don't care character(0 or 1). A geo cell can also be represented as a string encoding of thearea or point, for example, one possible encoding is base-32, whereevery 5 binary characters are encoded as an ASCII character.

Depending on latitude, the size of an area defined at a specified geocell precision can vary. When geohash is used for spatial indexing, theareas defined at various geo cell precisions are approximately:

GeoHash Length/Precision Width × Height 1 5,009.4 km × 4,992.6 km 21,252.3 km × 624.1 km  3 156.5 km × 156 km  4 39.1 km × 19.5 km 5 4.9 km× 4.9 km 6  1.2 km × 609.4 m 7 152.9 m × 152.4 m 8 38.2 m × 19 m  9 4.8m × 4.8 m 10  1.2 m × 59.5 cm 11 14.9 cm × 14.9 cm 12 3.7 cm × 1.9 cm

Other geo cell geometries, such as, hexagonal tiling, triangular tiling,etc. are also possible. For example, the H3 geospatial indexing systemis a multi-precision hexagonal tiling of a sphere (such as the Earth)indexed with hierarchical linear indexes.

In another aspect, geo cells are a hierarchical decomposition of asphere (such as the Earth) into representations of regions or pointsbased a Hilbert curve (e.g., the S2 hierarchy or other hierarchies).Regions/points of the sphere can be projected into a cube and each faceof the cube includes a quad-tree where the sphere point is projectedinto. After that, transformations can be applied and the spacediscretized. The geo cells are then enumerated on a Hilbert Curve (aspace-filling curve that converts multiple dimensions into one dimensionand preserves the approximate locality).

Due to the hierarchical nature of geo cells, any signal, event, entity,etc., associated with a geo cell of a specified precision is by defaultassociated with any less precise geo cells that contain the geo cell.For example, if a signal is associated with a geo cell of precision 9,the signal is by default also associated with corresponding geo cells ofprecisions 1, 2, 3, 4, 5, 6, 7, and 8. Similar mechanisms are applicableto other tiling and geo cell arrangements. For example, S2 has a celllevel hierarchy ranging from level zero (85,011,012 km²) to level 30(between 0.48 cm² to 0.96 cm²).

Signal Ingestion and Normalization

Signal ingestion modules ingest a variety of raw structured and/or rawunstructured signals on an on going basis and in essentially real-time.Raw signals can include social posts, live broadcasts, traffic camerafeeds, other camera feeds (e.g., from other public cameras or from CCTVcameras), listening device feeds, 911 calls, weather data, plannedevents, IoT device data, crowd sourced traffic and road information,satellite data, air quality sensor data, smart city sensor data, publicradio communication (e.g., among first responders and/or dispatchers,between air traffic controllers and pilots), etc. The content of rawsignals can include images, video, audio, text, etc.

In general, signal normalization can prepare (or pre-process) rawsignals into normalized signals to increase efficiency and effectivenessof subsequent computing activities, such as, event detection, eventnotification, etc., that utilize the normalized signals. For example,signal ingestion modules can normalize raw signals into normalizedsignals having a Time, Location, and Context (TLC) dimensions. An eventdetection infrastructure can use the Time, Location, and Contentdimensions to more efficiently and effectively detect events.

Per signal type and signal content, different normalization modules canbe used to extract, derive, infer, etc. Time, Location, and Contextdimensions from/for a raw signal. For example, one set of normalizationmodules can be configured to extract/derive/infer Time, Location andContext dimensions from/for social signals. Another set of normalizationmodules can be configured to extract/derive/infer Time, Location andContext dimensions from/for Web signals. A further set of normalizationmodules can be configured to extract/derive/infer Time, Location andContext dimensions from/for streaming signals.

Normalization modules for extracting/deriving/inferring Time, Location,and Context dimensions can include text processing modules, NLP modules,image processing modules, video processing modules, etc. The modules canbe used to extract/derive/infer data representative of Time, Location,and Context dimensions for a signal. Time, Location, and Contextdimensions for a signal can be extracted/derived/inferred from metadataand/or content of the signal.

For example, NLP modules can analyze metadata and content of a soundclip to identify a time, location, and keywords (e.g., fire, shooter,etc.). An acoustic listener can also interpret the meaning of sounds ina sound clip (e.g., a gunshot, vehicle collision, etc.) and convert torelevant context. Live acoustic listeners can determine the distance anddirection of a sound. Similarly, image processing modules can analyzemetadata and pixels in an image to identify a time, location andkeywords (e.g., fire, shooter, etc.). Image processing modules can alsointerpret the meaning of parts of an image (e.g., a person holding agun, flames, a store logo, etc.) and convert to relevant context. Othermodules can perform similar operations for other types of contentincluding text and video.

Per signal type, each set of normalization modules can differ but mayinclude at least some similar modules or may share some common modules.For example, similar (or the same) image analysis modules can be used toextract named entities from social signal images and public camerafeeds. Likewise, similar (or the same) NLP modules can be used toextract named entities from social signal text and web text.

In some aspects, an ingested signal includes sufficient expresslydefined time, location, and context information upon ingestion. Theexpressly defined time, location, and context information is used todetermine Time, Location, and Context dimensions for the ingestedsignal. In other aspects, an ingested signal lacks expressly definedlocation information or expressly defined location information isinsufficient (e.g., lacks precision) upon ingestion. In these otheraspects, Location dimension or additional Location dimension can beinferred from features of an ingested signal and/or through referencesto other data sources. In further aspects, an ingested signal lacksexpressly defined context information or expressly defined contextinformation is insufficient (e.g., lacks precision) upon ingestion. Inthese further aspects, Context dimension or additional Context dimensioncan be inferred from features of an ingested signal and/or throughreference to other data sources.

In further aspects, time information may not be included, or includedtime information may not be given with high enough precision and Timedimension is inferred. For example, a user may post an image to a socialnetwork which had been taken some indeterminate time earlier.

Normalization modules can use named entity recognition and reference toa geo cell database to infer Location dimension. Named entities can berecognized in text, images, video, audio, or sensor data. The recognizednamed entities can be compared to named entities in geo cell entries.Matches indicate possible signal origination in a geographic areadefined by a geo cell.

As such, a normalized signal can include a Time dimension, a Locationdimension, a Context dimension (e.g., single source probabilities andprobability details), a signal type, a signal source, and content.

A single source probability can be calculated by single sourceclassifiers (e.g., machine learning models, artificial intelligence,neural networks, statistical models, etc.) that consider hundreds,thousands, or even more signal features of a signal. Single sourceclassifiers can be based on binary models and/or multi-class models.

FIG. 1A depicts part of computer architecture 100 that facilitatesingesting and normalizing signals. As depicted, computer architecture100 includes signal ingestion modules 101, social signals 171, Websignals 172, and streaming signals 173. Signal ingestion modules 101,social signals 171, Web signals 172, and streaming signals 173 can beconnected to (or be part of) a network, such as, for example, a systembus, a Local Area Network (“LAN”), a Wide Area Network (“WAN”), and eventhe Internet. Accordingly, signal ingestion modules 101, social signals171, Web signals 172, and streaming signals 173 as well as any otherconnected computer systems and their components can create and exchangemessage related data (e.g., Internet Protocol (“IP”) datagrams and otherhigher layer protocols that utilize IP datagrams, such as, TransmissionControl Protocol (“TCP”), Hypertext Transfer Protocol (“HTTP”), SimpleMail Transfer Protocol (“SMTP”), Simple Object Access Protocol (SOAP),etc. or using other non-datagram protocols) over the network.

Signal ingestion module(s) 101 can ingest raw signals 121, includingsocial signals 171, web signals 172, and streaming signals 173 (e.g.,social posts, traffic camera feeds, other camera feeds, listening devicefeeds, 911 calls, weather data, planned events, IoT device data, crowdsourced traffic and road information, satellite data, air quality sensordata, smart city sensor data, public radio communication, etc.) on goingbasis and in essentially real-time. Signal ingestion module(s) 101include social content ingestion modules 174, web content ingestionmodules 176, stream content ingestion modules 177, and signal formatter180. Signal formatter 180 further includes social signal processingmodule 181, web signal processing module 182, and stream signalprocessing modules 183.

For each type of signal, a corresponding ingestion module and signalprocessing module can interoperate to normalize the signal into a Time,Location, Context (TLC) dimensions. For example, social contentingestion modules 174 and social signal processing module 181 caninteroperate to normalize social signals 171 into TLC dimensions.Similarly, web content ingestion modules 176 and web signal processingmodule 182 can interoperate to normalize web signals 172 into TLCdimensions. Likewise, stream content ingestion modules 177 and streamsignal processing modules 183 can interoperate to normalize streamingsignals 173 into TLC dimensions.

In one aspect, signal content exceeding specified size requirements(e.g., audio or video) is cached upon ingestion. Signal ingestionmodules 101 include a URL or other identifier to the cached contentwithin the context for the signal.

In one aspect, signal formatter 180 includes modules for determining asingle source probability as a ratio of signals turning into eventsbased on the following signal properties: (1) event class (e.g., fire,accident, weather, etc.), (2) media type (e.g., text, image, audio,etc.), (3) source (e.g., twitter, traffic camera, first responder radiotraffic, etc.), and (4) geo type (e.g., geo cell, region, or non-geo).Probabilities can be stored in a lookup table for different combinationsof the signal properties. Features of a signal can be derived and usedto query the lookup table. For example, the lookup table can be queriedwith terms (“accident”, “image”, “twitter”, “region”). The correspondingratio (probability) can be returned from the table.

In another aspect, signal formatter 180 includes a plurality of singlesource classifiers (e.g., artificial intelligence, machine learningmodules, neural networks, etc.). Each single source classifier canconsider hundreds, thousands, or even more signal features of a signal.Signal features of a signal can be derived and submitted to a signalsource classifier. The single source classifier can return a probabilitythat a signal indicates a type of event. Single source classifiers canbe binary classifiers or multi-source classifiers.

Raw classifier output can be adjusted to more accurately represent aprobability that a signal is a “true positive”. For example, 1,000signals whose raw classifier output is 0.9 may include 80% as truepositives. Thus, probability can be adjusted to 0.8 to reflect trueprobability of the signal being a true positive. “Calibration” can bedone in such a way that for any “calibrated score” this score reflectsthe true probability of a true positive outcome.

Signal ingestion modules 101 can insert one or more single sourceprobabilities and corresponding probability details into a normalizedsignal to represent a Context (C) dimension. Probability details canindicate a probabilistic model and features used to calculate theprobability. In one aspect, a probabilistic model and signal featuresare contained in a hash field.

Signal ingestion modules 101 can access “transdimensionality”transformations structured and defined in a “TLC” dimensional model.Signal ingestion modules 101 can apply the “transdimensionality”transformations to generic source data in raw signals to re-encode thesource data into normalized data having lower dimensionality.Dimensionality reduction can include reducing dimensionality of a rawsignal to a normalized signal including a T vector, an L vector, and a Cvector. At lower dimensionality, the complexity of measuring “distances”between dimensional vectors across different normalized signals isreduced.

Thus, in general, any received raw signals can be normalized intonormalized signals including a Time (T) dimension, a Location (L)dimension, a Context (C) dimension, signal source, signal type, andcontent. Signal ingestion modules 101 can send normalized signals 122 toevent detection infrastructure 103.

For example, signal ingestion modules 101 can send normalized signal122A, including time 123A, location 124A, context 126A, content 127A,type 128A, and source 129A to event detection infrastructure 103.Similarly, signal ingestion modules 101 can send normalized signal 122B,including time 123B, location 124B, context 126B, content 127B, type128B, and source 129B to event detection infrastructure 103.

Event Detection

FIG. 1B depicts part of computer architecture 100 that facilitatesdetecting events. As depicted, computer architecture 100 includes geocell database 111 and even notification 116. Geo cell database 111 andevent notification 116 can be connected to (or be part of) a networkwith signal ingestion modules 101 and event detection infrastructure103. As such, geo cell database 111 and even notification 116 can createand exchange message related data over the network.

As described, in general, on an ongoing basis, concurrently with signalingestion (and also essentially in real-time), event detectioninfrastructure 103 detects different categories of (planned andunplanned) events (e.g., fire, police response, mass shooting, trafficaccident, natural disaster, storm, active shooter, concerts, protests,etc.) in different locations (e.g., anywhere across a geographic area,such as, the United States, a State, a defined area, an impacted area,an area defined by a geo cell, an address, etc.), at different timesfrom Time, Location, and Context dimensions included in normalizedsignals. Since, normalized signals are normalized to include Time,Location, and Context dimensions, event detection infrastructure 103 canhandle normalized signals in a more uniform manner increasing eventdetection efficiency and effectiveness.

Event detection infrastructure 103 can also determine an eventtruthfulness, event severity, and an associated geo cell. In one aspect,a Context dimension in a normalized signal increases the efficiency andeffectiveness of determining truthfulness, severity, and an associatedgeo cell.

Generally, an event truthfulness indicates how likely a detected eventis actually an event (vs. a hoax, fake, misinterpreted, etc.).Truthfulness can range from less likely to be true to more likely to betrue. In one aspect, truthfulness is represented as a numerical value,such as, for example, from 1 (less truthful) to 10 (more truthful) or aspercentage value in a percentage range, such as, for example, from 0%(less truthful) to 100% (more truthful). Other truthfulnessrepresentations are also possible. For example, truthfulness can be adimension or represented by one or more vectors.

Generally, an event severity indicates how severe an event is (e.g.,what degree of badness, what degree of damage, etc. is associated withthe event). Severity can range from less severe (e.g., a single vehicleaccident without injuries) to more severe (e.g., multi vehicle accidentwith multiple injuries and a possible fatality). As another example, ashooting event can also range from less severe (e.g., one victim withoutlife threatening injuries) to more severe (e.g., multiple injuries andmultiple fatalities). In one aspect, severity is represented as anumerical value, such as, for example, from 1 (less severe) to 5 (moresevere). Other severity representations are also possible. For example,severity can be a dimension or represented by one or more vectors.

In general, event detection infrastructure 103 can include a geodetermination module including modules for processing different kinds ofcontent including location, time, context, text, images, audio, andvideo into search terms. The geo determination module can query a geocell database with search terms formulated from normalized signalcontent. The geo cell database can return any geo cells having matchingsupplemental information. For example, if a search term includes astreet name, a subset of one or more geo cells including the street namein supplemental information can be returned to the event detectioninfrastructure.

Event detection infrastructure 103 can use the subset of geo cells todetermine a geo cell associated with an event location. Eventsassociated with a geo cell can be stored back into an entry for the geocell in the geo cell database. Thus, over time an historical progressionof events within a geo cell can be accumulated.

As such, event detection infrastructure 103 can assign an event ID, anevent time, an event location, an event category, an event description,an event truthfulness, and an event severity to each detected event.Detected events can be sent to relevant entities, including to mobiledevices, to computer systems, to APIs, to data storage, etc.

Event detection infrastructure 103 detects events from informationcontained in normalized signals 122. Event detection infrastructure 103can detect an event from a single normalized signal 122 or from multiplenormalized signals 122. In one aspect, event detection infrastructure103 detects an event based on information contained in one or morenormalized signals 122. In another aspect, event detectioninfrastructure 103 detects a possible event based on informationcontained in one or more normalized signals 122. Event detectioninfrastructure 103 then validates the potential event as an event basedon information contained in one or more other normalized signals 122.

As depicted, event detection infrastructure 103 includes geodetermination module 104, categorization module 106, truthfulnessdetermination module 107, and severity determination module 108.

Geo determination module 104 can include NLP modules, image analysismodules, etc. for identifying location information from a normalizedsignal. Geo determination module 104 can formulate (e.g., location)search terms 141 by using NLP modules to process audio, using imageanalysis modules to process images, etc. Search terms can include streetaddresses, building names, landmark names, location names, school names,image fingerprints, etc. Event detection infrastructure 103 can use aURL or identifier to access cached content when appropriate.

Categorization module 106 can categorize a detected event into one of aplurality of different categories (e.g., fire, police response, massshooting, traffic accident, natural disaster, storm, active shooter,concerts, protests, etc.) based on the content of normalized signalsused to detect and/or otherwise related to an event.

Truthfulness determination module 107 can determine the truthfulness ofa detected event based on one or more of: source, type, age, and contentof normalized signals used to detect and/or otherwise related to theevent. Some signal types may be inherently more reliable than othersignal types. For example, video from a live traffic camera feed may bemore reliable than text in a social media post. Some signal sources maybe inherently more reliable than others. For example, a social mediaaccount of a government agency may be more reliable than a social mediaaccount of an individual. The reliability of a signal can decay overtime.

Severity determination module 108 can determine the severity of adetected event based on or more of: location, content (e.g., dispatchcodes, keywords, etc.), and volume of normalized signals used to detectand/or otherwise related to an event. Events at some locations may beinherently more severe than events at other locations. For example, anevent at a hospital is potentially more severe than the same event at anabandoned warehouse. Event category can also be considered whendetermining severity. For example, an event categorized as a “Shooting”may be inherently more severe than an event categorized as “PolicePresence” since a shooting implies that someone has been injured.

Geo cell database 111 includes a plurality of geo cell entries. Each geocell entry is included in a geo cell defining an area and correspondingsupplemental information about things included in the defined area. Thecorresponding supplemental information can include latitude/longitude,street names in the area defined by and/or beyond the geo cell,businesses in the area defined by the geo cell, other Areas of Interest(AOIs) (e.g., event venues, such as, arenas, stadiums, theaters, concerthalls, etc.) in the area defined by the geo cell, image fingerprintsderived from images captured in the area defined by the geo cell, andprior events that have occurred in the area defined by the geo cell. Forexample, geo cell entry 151 includes geo cell 152, lat/lon 153, streets154, businesses 155, AOIs 156, and prior events 157. Each event in priorevents 157 can include a location (e.g., a street address), a time(event occurrence time), an event category, an event truthfulness, anevent severity, and an event description. Similarly, geo cell entry 161includes geo cell 162, lat/lon 163, streets 164, businesses 165, AOIs166, and prior events 167. Each event in prior events 167 can include alocation (e.g., a street address), a time (event occurrence time), anevent category, an event truthfulness, an event severity, and an eventdescription.

Other geo cell entries can include the same or different (more or less)supplemental information, for example, depending on infrastructuredensity in an area. For example, a geo cell entry for an urban area cancontain more diverse supplemental information than a geo cell entry foran agricultural area (e.g., in an empty field).

Geo cell database 111 can store geo cell entries in a hierarchicalarrangement based on geo cell precision. As such, geo cell informationof more precise geo cells is included in the geo cell information forany less precise geo cells that include the more precise geo cell.

Geo determination module 104 can query geo cell database 111 with searchterms 141. Geo cell database 111 can identify any geo cells havingsupplemental information that matches search terms 141. For example, ifsearch terms 141 include a street address and a business name, geo celldatabase 111 can identify geo cells having the street name and businessname in the area defined by the geo cell. Geo cell database 111 canreturn any identified geo cells to geo determination module 104 in geocell subset 142.

Geo determination module can use geo cell subset 142 to determine thelocation of event 135 and/or a geo cell associated with event 135. Asdepicted, event 135 includes event ID 132, time 133, location 137,description 136, category 137, truthfulness 138, and severity 139.

Event detection infrastructure 103 can also determine that event 135occurred in an area defined by geo cell 162 (e.g., a geohash havingprecision of level 7 or level 9). For example, event detectioninfrastructure 103 can determine that location 134 is in the areadefined by geo cell 162. As such, event detection infrastructure 103 canstore event 135 in events 167 (i.e., historical events that haveoccurred in the area defined by geo cell 162).

Event detection infrastructure 103 can also send event 135 to eventnotification module 116. Event notification module 116 can notify one ormore entities about event 135.

FIG. 2 illustrates a flow chart of an example method 200 for normalizingingested signals. Method 200 will be described with respect to thecomponents and data in computer architecture 100.

Method 200 includes ingesting a raw signal including a time stamp, anindication of a signal type, an indication of a signal source, andcontent (201). For example, signal ingestion modules 101 can ingest araw signal 121 from one of: social signals 171, web signals 172, orstreaming signals 173.

Method 200 includes forming a normalized signal from characteristics ofthe raw signal (202). For example, signal ingestion modules 101 can forma normalized signal 122A from the ingested raw signal 121.

Forming a normalized signal includes forwarding the raw signal toingestion modules matched to the signal type and/or the signal source(203). For example, if ingested raw signal 121 is from social signals171, raw signal 121 can be forwarded to social content ingestion modules174 and social signal processing modules 181. If ingested raw signal 121is from web signals 172, raw signal 121 can be forwarded to web contentingestion modules 175 and web signal processing modules 182. If ingestedraw signal 121 is from streaming signals 173, raw signal 121 can beforwarded to streaming content ingestion modules 176 and streamingsignal processing modules 183.

Forming a normalized signal includes determining a time dimensionassociated with the raw signal from the time stamp (204). For example,signal ingestion modules 101 can determine time 123A from a time stampin ingested raw signal 121.

Forming a normalized signal includes determining a location dimensionassociated with the raw signal from one or more of: location informationincluded in the raw signal or from location annotations inferred fromsignal characteristics (205). For example, signal ingestion modules 101can determine location 124A from location information included in rawsignal 121 or from location annotations derived from characteristics ofraw signal 121 (e.g., signal source, signal type, signal content).

Forming a normalized signal includes determining a context dimensionassociated with the raw signal from one or more of: context informationincluded in the raw signal or from context signal annotations inferredfrom signal characteristics (206). For example, signal ingestion modules101 can determine context 126A from context information included in rawsignal 121 or from context annotations derived from characteristics ofraw signal 121 (e.g., signal source, signal type, signal content).

Forming a normalized signal includes inserting the time dimension, thelocation dimension, and the context dimension in the normalized signal(207). For example, signal ingestion modules 101 can insert time 123A,location 124A, and context 126A in normalized signal 122. Method 200includes sending the normalized signal to an event detectioninfrastructure (208). For example, signal ingestion modules 101 can sendnormalized signal 122A to event detection infrastructure 103.

FIGS. 3A, 3B, and 3C depict other example components that can beincluded in signal ingestion modules 101. Signal ingestion modules 101can include signal transformers for different types of signals includingsignal transformer 301A (for TLC signals), signal transformer 301B (forTL signals), and signal transformer 301C (for T signals). In one aspect,a single module combines the functionality of multiple different signaltransformers.

Signal ingestion modules 101 can also include location services 302,classification tag service 306, signal aggregator 308, context inferencemodule 312, and location inference module 316. Location services 302,classification tag service 306, signal aggregator 308, context inferencemodule 312, and location inference module 316 or parts thereof caninteroperate with and/or be integrated into any of ingestion modules174, web content ingestion modules 176, stream content ingestion modules177, social signal processing module 181, web signal processing module182, and stream signal processing modules 183. Location services 302,classification tag service 306, signal aggregator 308, context inferencemodule 312, and location inference module 316 can interoperate toimplement “transdimensionality” transformations to reduce raw signaldimensionality.

Signal ingestion modules 101 can also include storage for signals indifferent stages of normalization, including TLC signal storage 307, TLsignal storage 311, T signal storage 313, TC signal storage 314, andaggregated TLC signal storage 309. In one aspect, data ingestion modules101 implement a distributed messaging system. Each of signal storage307, 309, 311, 313, and 314 can be implemented as a message container(e.g., a topic) associated with a type of message.

FIG. 4 illustrates a flow chart of an example method 400 for normalizingan ingested signal including time information, location information, andcontext information. Method 400 will be described with respect to thecomponents and data in FIG. 3A.

Method 400 includes accessing a raw signal including a time stamp,location information, context information, an indication of a signaltype, an indication of a signal source, and content (401). For example,signal transformer 301A can access raw signal 221A. Raw signal 221Aincludes timestamp 231A, location information 232A (e.g., lat/lon, GPScoordinates, etc.), context information 233A (e.g., text expresslyindicating a type of event), signal type 227A (e.g., social media, 911communication, traffic camera feed, etc.), signal source 228A (e.g.,Facebook, twitter, Waze, etc.), and signal content 229A (e.g., one ormore of: image, video, text, keyword, locale, etc.).

Method 400 includes determining a Time dimension for the raw signal(402). For example, signal transformer 301A can determine time 223A fromtimestamp 231A.

Method 400 includes determining a Location dimension for the raw signal(403). For example, signal transformer 301A sends location information232A to location services 302. Geo cell service 303 can identify a geocell corresponding to location information 232A. Market service 304 canidentify a designated market area (DMA) corresponding to locationinformation 232A. Location services 302 can include the identified geocell and/or DMA in location 224A. Location services 302 return location224A to signal transformer 301.

Method 400 includes determining a Context dimension for the raw signal(404). For example, signal transformer 301A sends context information233A to classification tag service 306. Classification tag service 306identifies one or more classification tags 226A (e.g., fire, policepresence, accident, natural disaster, etc.) from context information233A. Classification tag service 306 returns classification tags 226A tosignal transformer 301A.

Method 400 includes inserting the Time dimension, the Locationdimension, and the Context dimension in a normalized signal (405). Forexample, signal transformer 301A can insert time 223A, location 224A,and tags 226A in normalized signal 222A (a TLC signal). Method 400includes storing the normalized signal in signal storage (406). Forexample, signal transformer 301A can store normalized signal 222A in TLCsignal storage 307. (Although not depicted, timestamp 231A, locationinformation 232A, and context information 233A can also be included (orremain) in normalized signal 222A).

Method 400 includes storing the normalized signal in aggregated storage(406). For example, signal aggregator 308 can aggregate normalizedsignal 222A along with other normalized signals determined to relate tothe same event. In one aspect, signal aggregator 308 forms a sequence ofsignals related to the same event. Signal aggregator 308 stores thesignal sequence, including normalized signal 222A, in aggregated TLCstorage 309 and eventually forwards the signal sequence to eventdetection infrastructure 103.

FIG. 5 illustrates a flow chart of an example method 500 for normalizingan ingested signal including time information and location information.Method 500 will be described with respect to the components and data inFIG. 3B.

Method 500 includes accessing a raw signal including a time stamp,location information, an indication of a signal type, an indication of asignal source, and content (501). For example, signal transformer 301Bcan access raw signal 221B. Raw signal 221B includes timestamp 231B,location information 232B (e.g., lat/lon, GPS coordinates, etc.), signaltype 227B (e.g., social media, 911 communication, traffic camera feed,etc.), signal source 228B (e.g., Facebook, twitter, Waze, etc.), andsignal content 229B (e.g., one or more of: image, video, audio, text,keyword, locale, etc.).

Method 500 includes determining a Time dimension for the raw signal(502). For example, signal transformer 301B can determine time 223B fromtimestamp 231B.

Method 500 includes determining a Location dimension for the raw signal(503). For example, signal transformer 301B sends location information232B to location services 302. Geo cell service 303 can be identify ageo cell corresponding to location information 232B. Market service 304can identify a designated market area (DMA) corresponding to locationinformation 232B. Location services 302 can include the identified geocell and/or DMA in location 224B. Location services 302 returns location224B to signal transformer 301.

Method 500 includes inserting the Time dimension and Location dimensioninto a signal (504). For example, signal transformer 301B can inserttime 223B and location 224B into TL signal 236B. (Although not depicted,timestamp 231B and location information 232B can also be included (orremain) in TL signal 236B). Method 500 includes storing the signal,along with the determined Time dimension and

Location dimension, to a Time, Location message container (505). Forexample, signal transformer 301B can store TL signal 236B to TL signalstorage 311. Method 500 includes accessing the signal from the Time,Location message container (506). For example, signal aggregator 308 canaccess TL signal 236B from TL signal storage 311.

Method 500 includes inferring context annotations based oncharacteristics of the signal (507). For example, context inferencemodule 312 can access TL signal 236B from TL signal storage 311. Contextinference module 312 can infer context annotations 241 fromcharacteristics of TL signal 236B, including one or more of: time 223B,location 224B, type 227B, source 228B, and content 229B. In one aspect,context inference module 212 includes one or more of: NLP modules, audioanalysis modules, image analysis modules, video analysis modules, etc.Context inference module 212 can process content 229B in view of time223B, location 224B, type 227B, source 228B, to infer contextannotations 241 (e.g., using machine learning, artificial intelligence,neural networks, machine classifiers, etc.). For example, if content229B is an image that depicts flames and a fire engine, contextinference module 212 can infer that content 229B is related to a fire.Context inference 212 module can return context annotations 241 tosignal aggregator 208.

Method 500 includes appending the context annotations to the signal(508). For example, signal aggregator 308 can append context annotations241 to TL signal 236B. Method 500 includes looking up classificationtags corresponding to the classification annotations (509). For example,signal aggregator 308 can send context annotations 241 to classificationtag service 306. Classification tag service 306 can identify one or moreclassification tags 226B (a Context dimension) (e.g., fire, policepresence, accident, natural disaster, etc.) from context annotations241. Classification tag service 306 returns classification tags 226B tosignal aggregator 308.

Method 500 includes inserting the classification tags in a normalizedsignal (510). For example, signal aggregator 308 can insert tags 226B (aContext dimension) into normalized signal 222B (a TLC signal). Method500 includes storing the normalized signal in aggregated storage (511).For example, signal aggregator 308 can aggregate normalized signal 222Balong with other normalized signals determined to relate to the sameevent. In one aspect, signal aggregator 308 forms a sequence of signalsrelated to the same event. Signal aggregator 308 stores the signalsequence, including normalized signal 222B, in aggregated TLC storage309 and eventually forwards the signal sequence to event detectioninfrastructure 103. (Although not depicted, timestamp 231B, locationinformation 232C, and context annotations 241 can also be included (orremain) in normalized signal 222B).

FIG. 6 illustrates a flow chart of an example method 600 for normalizingan ingested signal including time information and location information.Method 600 will be described with respect to the components and data inFIG. 3C.

Method 600 includes accessing a raw signal including a time stamp, anindication of a signal type, an indication of a signal source, andcontent (601). For example, signal transformer 301C can access rawsignal 221C. Raw signal 221C includes timestamp 231C, signal type 227C(e.g., social media, 911 communication, traffic camera feed, etc.),signal source 228C (e.g., Facebook, twitter, Waze, etc.), and signalcontent 229C (e.g., one or more of: image, video, text, keyword, locale,etc.).

Method 600 includes determining a Time dimension for the raw signal(602). For example, signal transformer 301C can determine time 223C fromtimestamp 231C. Method 600 includes inserting the Time dimension into aT signal (603). For example, signal transformer 301C can insert time223C into T signal 234C. (Although not depicted, timestamp 231C can alsobe included (or remain) in T signal 234C).

Method 600 includes storing the T signal, along with the determined Timedimension, to a Time message container (604). For example, signaltransformer 301C can store T signal 236C to T signal storage 313. Method600 includes accessing the T signal from the Time message container(605). For example, signal aggregator 308 can access T signal 234C fromT signal storage 313.

Method 600 includes inferring context annotations based oncharacteristics of the T signal (606). For example, context inferencemodule 312 can access T signal 234C from T signal storage 313. Contextinference module 312 can infer context annotations 242 fromcharacteristics of T signal 234C, including one or more of: time 223C,type 227C, source 228C, and content 229C. As described, contextinference module 212 can include one or more of: NLP modules, audioanalysis modules, image analysis modules, video analysis modules, etc.Context inference module 212 can process content 229C in view of time223C, type 227C, source 228C, to infer context annotations 242 (e.g.,using machine learning, artificial intelligence, neural networks,machine classifiers, etc.). For example, if content 229C is a videodepicting two vehicles colliding on a roadway, context inference module212 can infer that content 229C is related to an accident. Contextinference 212 module can return context annotations 242 to signalaggregator 208.

Method 600 includes appending the context annotations to the T signal(607). For example, signal aggregator 308 can append context annotations242 to T signal 234C. Method 600 includes looking up classification tagscorresponding to the classification annotations (608). For example,signal aggregator 308 can send context annotations 242 to classificationtag service 306. Classification tag service 306 can identify one or moreclassification tags 226C (a Context dimension) (e.g., fire, policepresence, accident, natural disaster, etc.) from context annotations242. Classification tag service 306 returns classification tags 226C tosignal aggregator 208.

Method 600 includes inserting the classification tags into a TC signal(609). For example, signal aggregator 308 can insert tags 226C into TCsignal 237C. Method 600 includes storing the TC signal to a Time,Context message container (610). For example, signal aggregator 308 canstore TC signal 237C in TC signal storage 314. (Although not depicted,timestamp 231C and context annotations 242 can also be included (orremain) in normalized signal 237C).

Method 600 includes inferring location annotations based oncharacteristics of the TC signal (611). For example, location inferencemodule 316 can access TC signal 237C from TC signal storage 314.Location inference module 316 can include one or more of: NLP modules,audio analysis modules, image analysis modules, video analysis modules,etc. Location inference module 316 can process content 229C in view oftime 223C, type 227C, source 228C, and classification tags 226C (andpossibly context annotations 242) to infer location annotations 243(e.g., using machine learning, artificial intelligence, neural networks,machine classifiers, etc.). For example, if content 229C is a videodepicting two vehicles colliding on a roadway, the video can include anearby street sign, business name, etc. Location inference module 316can infer a location from the street sign, business name, etc. Locationinference module 316 can return location annotations 243 to signalaggregator 308.

Method 600 includes appending the location annotations to the TC signalwith location annotations (612). For example, signal aggregator 308 canappend location annotations 243 to TC signal 237C. Method 600determining a Location dimension for the TC signal (613). For example,signal aggregator 308 can send location annotations 243 to locationservices 302. Geo cell service 303 can identify a geo cell correspondingto location annotations 243. Market service 304 can identify adesignated market area (DMA) corresponding to location annotations 243.Location services 302 can include the identified geo cell and/or DMA inlocation 224C. Location services 302 returns location 224C to signalaggregation services 308.

Method 600 includes inserting the Location dimension into a normalizedsignal (614). For example, signal aggregator 308 can insert location224C into normalized signal 222C. Method 600 includes storing thenormalized signal in aggregated storage (615). For example, signalaggregator 308 can aggregate normalized signal 222C along with othernormalized signals determined to relate to the same event. In oneaspect, signal aggregator 308 forms a sequence of signals related to thesame event. Signal aggregator 308 stores the signal sequence, includingnormalized signal 222C, in aggregated TLC storage 309 and eventuallyforwards the signal sequence to event detection infrastructure 103.(Although not depicted, timestamp 231B, context annotations 241, andlocation annotations 24, can also be included (or remain) in normalizedsignal 222B).

In another aspect, a Location dimension is determined prior to a Contextdimension when a T signal is accessed. A Location dimension (e.g., geocell and/or DMA) and/or location annotations are used when inferringcontext annotations.

Accordingly, location services 202 can identify a geo cell and/or DMAfor a signal from location information in the signal and/or frominferred location annotations. Similarly, classification tag service 206can identify classification tags for a signal from context informationin the signal and/or from inferred context annotations.

Signal aggregator 208 can concurrently handle a plurality of signals ina plurality of different stages of normalization. For example, signalaggregator 208 can concurrently ingest and/or process a plurality Tsignals, a plurality of TL signals, a plurality of TC signals, and aplurality of TLC signals. Accordingly, aspects of the inventionfacilitate acquisition of live, ongoing forms of data into an eventdetection system with signal aggregator 208 acting as an “air trafficcontroller” of live data. Signals from multiple sources of data can beaggregated and normalized for a common purpose (e.g., of eventdetection). Data ingestion, event detection, and event notification canprocess data through multiple stages of logic with concurrency.

As such, a unified interface can handle incoming signals and content ofany kind. The interface can handle live extraction of signals acrossdimensions of time, location, and context. In some aspects, heuristicprocesses are used to determine one or more dimensions. Acquired signalscan include text and images as well as live-feed binaries, includinglive media in audio, speech, fast still frames, video streams, etc.

Signal normalization enables the world's live signals to be collected atscale and analyzed for detection and validation of live events happeningglobally. A data ingestion and event detection pipeline aggregatessignals and combines detections of various strengths into truthfulevents. Thus, normalization increases event detection efficiencyfacilitating event detection closer to “live time” or at “moment zero”.

The present described aspects may be implemented in other specific formswithout departing from its spirit or essential characteristics. Thedescribed aspects are to be considered in all respects only asillustrative and not restrictive. The scope is, therefore, indicated bythe appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

1. A method comprising: ingesting a raw signal including source data ina plurality of data dimensions; applying a transdimensionality transformto the raw signal recoding and normalizing the source data into anormalized signal that includes normalized data in a common reducedplurality of dimensions including a time dimension, a locationdimension, and a context dimension, comprising: inferring a signalannotation from the source data and other signal characteristics of theraw signal; and deriving the time dimension, the location dimension, andthe context dimension from a combination of the other signalcharacteristics and the signal annotation, the deriving including atleast: computing a single source probability value for the raw signal,from a plurality of the other signal characteristics of the raw signal,that at least approximates a probability that the raw signal actuallyindicates an occurrence of a real-world event type; and inserting theprobability into the context dimension of the normalized data; detectinga real-world event of the real-world event type from the normalized datain the time dimension, the location dimension, and the context dimensionincluding at least the single source probability value; and notifying anentity about the real-world event.
 2. The method of claim 1, furthercomprising accessing the transdimensionality transform defined andstructured in a normalization dimensional model.
 3. The method of claim1, further comprising: ingesting another raw signal including othersource data in a further plurality of data dimensions; and applying thetransdimensionality transform to the other raw signal recoding andnormalizing the other source data into another normalized signal thatincludes other normalized data in the common reduced plurality ofdimensions including the time dimension, the location dimension, and thecontext dimension; and wherein detecting the real-world event of thereal-world event type comprises detecting the real-world event from theother normalized data in the time dimension, the location dimension, andthe context dimension included in the other normalized signal.
 4. Themethod of claim 3, wherein ingesting a raw signal comprises ingestingthe raw signal from a social media network source; and wherein ingestinganother raw signal comprises ingesting the other raw signal from asource other than the social media network source.
 5. The method ofclaim 4, wherein ingesting the other raw signal from a source other thanthe social media network source comprises ingesting the other raw signalfrom one of: a camera feed, a listening device feed, weather data, IoTdevice data, crowd sourced traffic information, a 911 call, satellitedata, air quality sensor data, smart city sensor data, or public radiocommunication.
 6. The method of claim 1, wherein deriving the timedimension, the location dimension, and the context dimension comprises:computing probability details indicating a probabilistic model used tocalculate the probability; and including the probability details in thecontext dimension; and wherein detecting the real-world event of thereal-world event type comprises detecting the real-world event from theprobability and the probability details.
 7. The method of claim 1,wherein computing a probability value comprises computing a probabilityvalue that at least approximates a probability of a real-world eventtype selected from among: a fire, police presence, an accident, anatural disaster, weather, a shooter, a concert, or a protest; whereindetecting a real-world event of the real-world event type comprisesdetecting the real-world event of the real-world event type selectedfrom among: the fire, the police presence, the accident, the naturaldisaster, the weather, the shooter, the concert, or the protest; andwherein notifying an entity about the real-world event comprisesnotifying one of: a person, a business entity, or a governmental agency.8. A computer system comprising: a processor; system memory coupled tothe processor and storing instructions configured to cause the processorto: ingest a raw signal including source data in a plurality of datadimensions; apply a transdimensionality transform to the raw signalrecoding and normalizing the source data into a normalized signal thatincludes normalized data in a common reduced plurality of dimensionsincluding a time dimension, a location dimension, and a contextdimension, comprising: infer a signal annotation from the source dataand other signal characteristics of the raw signal; and derive the timedimension, the location dimension, and the context dimension from boththe other signal characteristics and the signal annotation, the derivingincluding at least: compute a single source probability value for theraw signal, from a plurality of the other signal characteristics of theraw signal, that at least approximates a probability that the raw signalactually indicates an occurrence of a real-world event type; and insertthe probability into the context dimension of the normalized data;detect a real-world event of the real-world event type from thenormalized data in the time dimension, the location dimension, and thecontext dimension including at least the single source probabilityvalue; and notify an entity about the real-world event.
 9. The computersystem of claim 8, further comprising instructions configured to accessthe transdimensionality transform defined and structured in anormalization dimensional model.
 10. The computer system of claim 8,further comprising instructions configured to: ingest another raw signalincluding other source data in a further plurality of data dimensions;and apply the transdimensionality transform to the other raw signalrecoding and normalizing the other source data into another normalizedsignal that includes other normalized data in the common reducedplurality of dimensions including the time dimension, the locationdimension, and the context dimension; and wherein instructionsconfigured to detect the real-world event of the real-world event typecomprises instructions configured to detect the real-world event fromthe other normalized data in the time dimension, the location dimension,and the context dimension included in the other normalized signal. 11.The computer system of claim 8, wherein instructions configured toingest a raw signal comprise instructions configured to ingest the rawsignal from a social media network source; and wherein instructionsconfigured to ingest another raw signal comprises instructionsconfigured to ingest the other raw signal from a source other than thesocial media network source.
 12. The computer system of claim 11,wherein instructions configured to ingest the other raw signal from asource other than the social media network source comprise instructionsconfigured to ingest the other raw signal from one of: a camera feed, alistening device feed, weather data, IoT device data, crowd sourcedtraffic information, a 911 call, satellite data, air quality sensordata, smart city sensor data, or public radio communication.
 13. Thecomputer system of claim 8, wherein instructions configured to derivethe time dimension, the location dimension, and the context dimensioncomprise instructions configured to: compute probability detailsindicating a probabilistic model used to calculate the probability; andincluding the probability details in the context dimension; and whereininstructions configured to detect the real-world event of the real-worldevent type comprise instructions configured to detect the real-worldevent from the probability and the probability details.
 14. The computersystem of claim 8, wherein instructions configured to compute aprobability value comprises instructions configured to compute aprobability value that at least approximates a probability of areal-world event type selected from among: a fire, police presence, anaccident, a natural disaster, weather, a shooter, a concert, or aprotest; wherein instructions configured to detect a real-world event ofthe real-world event type comprise instructions configured to detect thereal-world event of the real-world event type selected from among: thefire, the police presence, the accident, the natural disaster, theweather, the shooter, the concert, or the protest; and whereininstructions configured to notify an entity about the real-world eventcomprise instructions configured to notify one of: a person, a businessentity, or a governmental agency. 13-20. (canceled)
 21. The method ofclaim 1, wherein the single source probability value is derived based ona consideration of one or more of a source, a type, an age, or a contentof the normalized signal.
 22. The method of claim 21, furthercomprising: ingesting a second raw signal including second source datain a plurality of data dimensions; applying a transdimensionalitytransform to the second raw signal to generate a second normalizedsignal; deriving a second time dimension, a second location dimension,and a second context dimension for the second normalized signal; andcomputing a second single source probability for the second raw signalfrom the second normalized signal, wherein the real-world event of thereal-world event type is detected using both the single sourceprobability value for the raw signal and the second single sourceprobability for the second raw signal.
 23. The method of claim 22,wherein the normalized signal and the second normalized signal arederived from different raw signal source types.
 24. The method of claim23, wherein the raw signal is one of a social signal, a web signal, or astreaming signal and the second raw signal is a different one of thesocial signal, the web signal, or the streaming signal.
 25. The methodof claim 1, wherein the single source probability value is derived fromsignal characteristics within the context dimension of the normalizedsignal.
 26. The method of claim 1, further comprising updating thesingle source probability value based on a time-decay function.